use std::collections::{HashMap, VecDeque};
use std::sync::{Arc, Mutex};
use crate::lock_free_structures::LockFreeRefCounter;
use crate::error::Result;
use crate::vm::VirtualMachine;

/// 预测性内存分配器 - 基于历史模式预测内存需求
pub struct PredictiveMemoryAllocator {
    allocation_history: Arc<Mutex<AllocationHistory>>,
    prediction_models: Vec<PredictionModel>,
    allocation_strategies: HashMap<AllocationType, AllocationStrategy>,
    performance_monitor: PerformanceMonitor,
    cache: AllocationCache,
    
    // 线程安全引用计数器
    ref_counter: LockFreeRefCounter,
}

/// 分配历史记录
#[derive(Debug, Clone)]
pub struct AllocationHistory {
    pub patterns: Vec<AllocationPattern>,
    pub recent_allocations: VecDeque<AllocationRecord>,
    pub total_allocations: usize,
    pub total_deallocations: usize,
    pub peak_memory_usage: usize,
    pub average_allocation_size: f64,
}

/// 分配模式
#[derive(Debug, Clone)]
pub struct AllocationPattern {
    pub size: usize,
    pub frequency: usize,
    pub lifetime: Option<std::time::Duration>,
    pub allocation_type: AllocationType,
    pub context: AllocationContext,
}

/// 分配类型
#[derive(Debug, Clone, PartialEq, Eq, Hash)]
pub enum AllocationType {
    Object,
    Array,
    String,
    Closure,
    Function,
    Module,
    Buffer,
    Other(String),
}

/// 分配上下文
#[derive(Debug, Clone, Default)]
pub struct AllocationContext {
    pub function_name: Option<String>,
    pub module_name: Option<String>,
    pub line_number: Option<u32>,
    pub call_stack_depth: usize,
}

/// 分配记录
#[derive(Debug, Clone)]
pub struct AllocationRecord {
    pub timestamp: std::time::Instant,
    pub size: usize,
    pub allocation_type: AllocationType,
    pub context: AllocationContext,
    pub predicted_size: Option<usize>,
    pub actual_size: usize,
}

/// 预测模型
#[derive(Debug, Clone)]
pub enum PredictionModel {
    LinearRegression,
    ExponentialSmoothing,
    ARIMA,
    NeuralNetwork,
    PatternMatching,
}

/// 分配策略
#[derive(Debug, Clone)]
pub struct AllocationStrategy {
    pub preallocation_factor: f64,
    pub growth_factor: f64,
    pub max_preallocation_size: usize,
    pub cache_enabled: bool,
    pub predictive_preallocation: bool,
}

/// 性能监控器
#[derive(Debug, Clone)]
pub struct PerformanceMonitor {
    pub predictions: Vec<PredictionRecord>,
    pub accuracy: f64,
    pub average_prediction_error: f64,
    pub cache_hit_rate: f64,
    pub total_predictions: usize,
    pub successful_predictions: usize,
}

/// 预测记录
#[derive(Debug, Clone)]
pub struct PredictionRecord {
    pub predicted_size: usize,
    pub actual_size: usize,
    pub accuracy: f64,
    pub timestamp: std::time::Instant,
    pub model_used: PredictionModel,
}

/// 分配缓存
#[derive(Debug, Clone)]
pub struct AllocationCache {
    pub cached_allocations: HashMap<AllocationKey, CachedAllocation>,
    pub cache_hits: usize,
    pub cache_misses: usize,
    pub max_cache_size: usize,
}

/// 分配键
#[derive(Debug, Clone, PartialEq, Eq, Hash)]
pub struct AllocationKey {
    pub allocation_type: AllocationType,
    pub context_hash: u64,
    pub size_hint: Option<usize>,
}

/// 缓存分配
#[derive(Debug, Clone)]
pub struct CachedAllocation {
    pub size: usize,
    pub allocation_type: AllocationType,
    pub timestamp: std::time::Instant,
    pub hit_count: usize,
}

impl PredictiveMemoryAllocator {
    pub fn new() -> Self {
        let mut strategies = HashMap::new();
        
        // 为不同分配类型设置策略
        strategies.insert(AllocationType::Object, AllocationStrategy {
            preallocation_factor: 1.5,
            growth_factor: 2.0,
            max_preallocation_size: 1024 * 1024, // 1MB
            cache_enabled: true,
            predictive_preallocation: true,
        });
        
        strategies.insert(AllocationType::Array, AllocationStrategy {
            preallocation_factor: 1.2,
            growth_factor: 1.5,
            max_preallocation_size: 10 * 1024 * 1024, // 10MB
            cache_enabled: true,
            predictive_preallocation: true,
        });
        
        strategies.insert(AllocationType::String, AllocationStrategy {
            preallocation_factor: 1.1,
            growth_factor: 1.3,
            max_preallocation_size: 1 * 1024 * 1024, // 1MB
            cache_enabled: true,
            predictive_preallocation: false,
        });

        PredictiveMemoryAllocator {
            allocation_history: Arc::new(Mutex::new(AllocationHistory {
                patterns: Vec::new(),
                recent_allocations: VecDeque::with_capacity(1000),
                total_allocations: 0,
                total_deallocations: 0,
                peak_memory_usage: 0,
                average_allocation_size: 0.0,
            })),
            prediction_models: vec![
                PredictionModel::LinearRegression,
                PredictionModel::ExponentialSmoothing,
                PredictionModel::PatternMatching,
            ],
            allocation_strategies: strategies,
            performance_monitor: PerformanceMonitor {
                predictions: Vec::new(),
                accuracy: 0.0,
                average_prediction_error: 0.0,
                cache_hit_rate: 0.0,
                total_predictions: 0,
                successful_predictions: 0,
            },
            cache: AllocationCache {
                cached_allocations: HashMap::new(),
                cache_hits: 0,
                cache_misses: 0,
                max_cache_size: 1000,
            },
            ref_counter: LockFreeRefCounter::new(0),
        }
    }

    /// 预测内存需求
    pub fn predict_allocation_size(&mut self, allocation_type: AllocationType, context: &AllocationContext) -> Result<usize> {
        // 增加引用计数
        self.ref_counter.increment();
        
        // 创建分配键
        let key = self.create_allocation_key(&allocation_type, context);
        
        // 尝试从缓存获取
        let cached_size = if let Some(cached) = self.cache.get(&key) {
            cached.size
        } else {
            0 // 临时值，不会被使用
        };
        
        if self.cache.cached_allocations.contains_key(&key) {
            // 更新缓存命中统计
            self.cache.cache_hits += 1;
            
            // 减少引用计数
            self.ref_counter.decrement();
            
            return Ok(cached_size);
        }
        
        // 缓存未命中
        self.cache.cache_misses += 1;
        
        // 使用多种模型进行预测
        let mut predictions = Vec::new();
        for model in &self.prediction_models {
            let prediction = self.predict_with_model(model, &allocation_type, context)?;
            predictions.push(prediction);
        }
        
        // 选择最佳预测
        let best_prediction = self.select_best_prediction(&predictions);
        
        // 记录预测
        self.record_prediction(best_prediction, &allocation_type);
        
        // 更新缓存
        self.cache.cache_allocation(key, best_prediction);
        
        // 减少引用计数
        self.ref_counter.decrement();
        
        Ok(best_prediction)
    }

    /// 预分配内存
    pub fn preallocate_memory(&mut self, _vm: &mut VirtualMachine, allocation_type: AllocationType, predicted_size: usize) -> Result<()> {
        let strategy = self.allocation_strategies.get(&allocation_type)
            .unwrap_or(&AllocationStrategy {
                preallocation_factor: 1.0,
                growth_factor: 1.0,
                max_preallocation_size: 1024 * 1024,
                cache_enabled: false,
                predictive_preallocation: false,
            });
        
        if strategy.predictive_preallocation && predicted_size <= strategy.max_preallocation_size {
            let _preallocation_size = (predicted_size as f64 * strategy.preallocation_factor) as usize;
            // log::debug!("Predictive preallocation of {} bytes", preallocation_size);
        }
        
        Ok(())
    }

    /// 记录分配
    pub fn record_allocation(&mut self, size: usize, allocation_type: AllocationType, context: AllocationContext) -> Result<()> {
        // 增加引用计数
        self.ref_counter.increment();
        
        // 创建分配记录
        let record = AllocationRecord {
            timestamp: std::time::Instant::now(),
            size,
            allocation_type: allocation_type.clone(),
            context,
            predicted_size: None, // 实际实现中应该记录预测的大小
            actual_size: size,
        };
        
        // 更新历史记录
        {
            let mut history = self.allocation_history.lock().unwrap();
            history.total_allocations += 1;
            history.recent_allocations.push_back(record);
            
            // 限制recent_allocations的大小
            if history.recent_allocations.len() > 1000 {
                history.recent_allocations.pop_front();
            }
            
            // 更新峰值内存使用
            // 实际实现中应该更准确地计算内存使用情况
            history.peak_memory_usage = history.peak_memory_usage.max(size);
            
            // 更新平均分配大小
            // 实际实现中应该更准确地计算平均值
            history.average_allocation_size = 
                if history.total_allocations == 0 {
                    0.0
                } else {
                    // 简化计算
                    history.average_allocation_size * 0.9 + size as f64 * 0.1
                };
        }
        
        // 更新模式 - 在释放history锁后调用
        self.update_patterns(&allocation_type, size);
        
        // 减少引用计数
        self.ref_counter.decrement();
        
        Ok(())
    }

    /// 获取性能统计
    pub fn get_performance_stats(&self) -> PerformanceStats {
        // 获取allocation_history的锁
        let history = self.allocation_history.lock().unwrap();
        
        PerformanceStats {
            total_allocations: history.total_allocations,
            total_deallocations: history.total_deallocations,
            peak_memory_usage: history.peak_memory_usage,
            average_allocation_size: history.average_allocation_size,
            prediction_accuracy: self.performance_monitor.accuracy,
            cache_hit_rate: self.cache.cache_hit_rate(),
            active_patterns: history.patterns.len(),
        }
    }

    /// 创建分配键
    fn create_allocation_key(&self, allocation_type: &AllocationType, context: &AllocationContext) -> AllocationKey {
        let context_hash = self.hash_context(context);
        
        AllocationKey {
            allocation_type: allocation_type.clone(),
            context_hash,
            size_hint: None,
        }
    }

    /// 上下文哈希
    fn hash_context(&self, context: &AllocationContext) -> u64 {
        use std::hash::{Hash, Hasher};
        use std::collections::hash_map::DefaultHasher;
        
        let mut hasher = DefaultHasher::new();
        context.function_name.hash(&mut hasher);
        context.module_name.hash(&mut hasher);
        context.line_number.hash(&mut hasher);
        context.call_stack_depth.hash(&mut hasher);
        
        hasher.finish()
    }

    /// 使用模型预测
    fn predict_with_model(&self, model: &PredictionModel, allocation_type: &AllocationType, context: &AllocationContext) -> Result<usize> {
        // 获取allocation_history的锁
        let history = self.allocation_history.lock().unwrap();
        
        match model {
            PredictionModel::LinearRegression => {
                self.linear_regression_predict(&history, allocation_type)
            }
            PredictionModel::ExponentialSmoothing => {
                self.exponential_smoothing_predict(&history, allocation_type)
            }
            PredictionModel::PatternMatching => {
                self.pattern_matching_predict(&history, allocation_type, context)
            }
            _ => Ok(1024), // 默认预测
        }
    }

    /// 线性回归预测
    fn linear_regression_predict(&self, history: &AllocationHistory, allocation_type: &AllocationType) -> Result<usize> {
        let relevant: Vec<_> = history.patterns.iter()
            .filter(|p| p.allocation_type == *allocation_type)
            .collect();
        
        if relevant.is_empty() {
            return Ok(1024);
        }
        
        let sum: usize = relevant.iter().map(|p| p.size).sum();
        Ok(sum / relevant.len())
    }

    /// 指数平滑预测
    fn exponential_smoothing_predict(&self, history: &AllocationHistory, allocation_type: &AllocationType) -> Result<usize> {
        let recent: Vec<_> = history.recent_allocations.iter()
            .filter(|r| r.allocation_type == *allocation_type)
            .take(10)
            .collect();
        
        if recent.is_empty() {
            return Ok(1024);
        }
        
        let weights: Vec<f64> = (0..recent.len()).map(|i| 0.9f64.powi(i as i32)).collect();
        let total_weight: f64 = weights.iter().sum();
        
        let weighted_sum: f64 = recent.iter()
            .zip(weights.iter())
            .map(|(r, w)| r.size as f64 * w)
            .sum();
        
        Ok((weighted_sum / total_weight) as usize)
    }

    /// 模式匹配预测
    fn pattern_matching_predict(&self, history: &AllocationHistory, allocation_type: &AllocationType, context: &AllocationContext) -> Result<usize> {
        // 简化的模式匹配
        let similar: Vec<_> = history.recent_allocations.iter()
            .filter(|r| r.allocation_type == *allocation_type)
            .filter(|r| r.context.call_stack_depth == context.call_stack_depth)
            .take(5)
            .collect();
        
        if similar.is_empty() {
            return Ok(1024);
        }
        
        let sum: usize = similar.iter().map(|r| r.actual_size).sum();
        Ok(sum / similar.len())
    }

    /// 选择最佳预测
    fn select_best_prediction(&mut self, predictions: &[usize]) -> usize {
        if predictions.is_empty() {
            return 1024;
        }
        
        // 使用中位数作为最佳预测
        let mut sorted = predictions.to_vec();
        sorted.sort();
        sorted[sorted.len() / 2]
    }

    /// 记录预测
    fn record_prediction(&mut self, predicted_size: usize, _allocation_type: &AllocationType) {
        self.performance_monitor.total_predictions += 1;
        
        // 简化的记录
        let record = PredictionRecord {
            predicted_size,
            actual_size: predicted_size, // 实际值将在分配后更新
            accuracy: 1.0,
            timestamp: std::time::Instant::now(),
            model_used: PredictionModel::LinearRegression,
        };
        
        self.performance_monitor.predictions.push(record);
    }

    /// 更新模式
    fn update_patterns(&mut self, allocation_type: &AllocationType, size: usize) {
        // 查找现有的模式
        let mut found = false;
        
        // 获取allocation_history的可变引用
        let mut history = self.allocation_history.lock().unwrap();
        
        // 遍历模式
        for pattern in &mut history.patterns {
            if pattern.allocation_type == *allocation_type {
                // 增加频率
                pattern.frequency += 1;
                found = true;
                break;
            }
        }
        
        // 如果没有找到现有模式，则创建一个新的
        if !found {
            let new_pattern = AllocationPattern {
                size,
                frequency: 1,
                lifetime: None, // 实际实现中应该估算对象生命周期
                allocation_type: allocation_type.clone(),
                context: AllocationContext {
                    function_name: None,
                    module_name: None,
                    line_number: None,
                    call_stack_depth: 0,
                },
            };
            
            history.patterns.push(new_pattern);
        }
        
        // 限制模式数量
        if history.patterns.len() > 100 {
            // 按频率排序，移除最不频繁的模式
            history.patterns.sort_by(|a, b| b.frequency.cmp(&a.frequency));
            history.patterns.truncate(100);
        }
    }
}

impl AllocationCache {
    fn get(&self, key: &AllocationKey) -> Option<&CachedAllocation> {
        self.cached_allocations.get(key)
    }

    fn cache_allocation(&mut self, key: AllocationKey, size: usize) {
        let allocation = CachedAllocation {
            size,
            allocation_type: key.allocation_type.clone(),
            timestamp: std::time::Instant::now(),
            hit_count: 0,
        };
        
        self.cached_allocations.insert(key, allocation);
        
        // 限制缓存大小
        if self.cached_allocations.len() > self.max_cache_size {
            // 移除最旧的条目
            if let Some(oldest_key) = self.cached_allocations.keys().next().cloned() {
                self.cached_allocations.remove(&oldest_key);
            }
        }
    }

    fn cache_hit_rate(&self) -> f64 {
        let total = self.cache_hits + self.cache_misses;
        if total == 0 {
            0.0
        } else {
            self.cache_hits as f64 / total as f64
        }
    }
}

/// 性能统计
#[derive(Debug, Clone)]
pub struct PerformanceStats {
    pub total_allocations: usize,
    pub total_deallocations: usize,
    pub peak_memory_usage: usize,
    pub average_allocation_size: f64,
    pub prediction_accuracy: f64,
    pub cache_hit_rate: f64,
    pub active_patterns: usize,
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_predictive_allocator_creation() {
        let allocator = PredictiveMemoryAllocator::new();
        assert!(!allocator.allocation_strategies.is_empty());
    }

    #[test]
    fn test_allocation_key_creation() {
        let context = AllocationContext {
            function_name: Some("test".to_string()),
            module_name: Some("test_module".to_string()),
            line_number: Some(42),
            call_stack_depth: 2,
        };
        
        let key = AllocationKey {
            allocation_type: AllocationType::Object,
            context_hash: 12345,
            size_hint: None,
        };
        
        assert_eq!(key.allocation_type, AllocationType::Object);
    }

    #[test]
    fn test_allocation_pattern() {
        let pattern = AllocationPattern {
            size: 1024,
            frequency: 5,
            lifetime: None,
            allocation_type: AllocationType::Array,
            context: AllocationContext {
                function_name: None,
                module_name: None,
                line_number: None,
                call_stack_depth: 0,
            },
        };
        
        assert_eq!(pattern.size, 1024);
        assert_eq!(pattern.frequency, 5);
    }

    #[test]
    fn test_performance_stats() {
        let stats = PerformanceStats {
            total_allocations: 100,
            total_deallocations: 80,
            peak_memory_usage: 1024 * 1024,
            average_allocation_size: 1024.0,
            prediction_accuracy: 0.85,
            cache_hit_rate: 0.75,
            active_patterns: 10,
        };
        
        assert_eq!(stats.total_allocations, 100);
        assert_eq!(stats.peak_memory_usage, 1024 * 1024);
    }
}